摘要
根据小波分析和盲信号分离原理,提出了一种金属地震资料降噪的盲小波算法。首先将金属地震信号用小波分解为不同频带的信号;其次把不同频带的信号进行软阈值法处理,并进一步对不同频带信号进行盲分离,提取出与源信号相关的信号;最后通过小波重构估计源信号。通过盲小波算法与其他降噪技术对实际金属地震资料进行降噪处理,结果表明盲小波算法能有效消除各种干扰噪声。去噪后的金属地震资料纹理清晰,地震资料剖面信噪比显著提高。
Based on the wavelet analysis and blind source separation, this paper proposes a blind wavelet algorithm to eliminate the metal ore deposit seismic data noises. This new method contains the following three main steps. Firstly, the metal ore deposit seismic signal is decomposed into different frequency band signals by using wavelet decomposition, secondly, it needs to use soft threshold to dispose different frequency band signals, and further to separate it by the blind source separation, meanwhile the different frequency band signals are extracted through an appropriate manner, and finally, the source signal is obtained by the reconstruction signal of wavelet transform. Through the blind wavelet algorithm and the other noise reduction technology, the processing of reducing noises for the actual metal ore deposit seismic data is done. This experimental result shows that the blind wavelet algorithm can eliminate various interference noises effectively. Then, the metal ore deposit seismic data texture is clear, and the signal-to-noise ratio of the seismic data section increases significantly.
出处
《成都理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2013年第2期120-124,共5页
Journal of Chengdu University of Technology: Science & Technology Edition
基金
中国地质调查项目(1212010916040)
关键词
金属矿地震资料
去噪
小波分析
盲信号分离
metal ore deposits
seismic data
de-noising
wavelet analysis
blind signal separation
